# Targeted Example Generation for Compilation Errors

arXiv: https://arxiv.org/abs/1909.00769 · HTML: https://mlsanity.com/paper/1909.00769/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Sep 2019.

## Built on by (most cited first, 3 of 3)

- [InvAASTCluster: On Applying Invariant-Based Program Clustering to Introductory Programming Assignments](https://mlsanity.com/paper/2206.14175/) (2022)
- [Automating Program Structure Classification](https://mlsanity.com/paper/2101.10087/) (2021)
- [Reimagining Application User Interface (UI) Design using Deep Learning Methods: Challenges and Opportunities](https://mlsanity.com/paper/2303.13055/) (2023)

## Abstract (arXiv)

We present TEGCER, an automated feedback tool for novice programmers. TEGCER uses supervised classification to match compilation errors in new code submissions with relevant pre-existing errors, submitted by other students before. The dense neural network used to perform this classification task is trained on 15000+ error-repair code examples. The proposed model yields a test set classification Pred@3 accuracy of 97.7% across 212 error category labels. Using this model as its base, TEGCER presents students with the closest relevant examples of solutions for their specific error on demand.